Analysing High-Level Help-Seeking Behaviour in ITSs [chapter]

Moffat Mathews, Tanja Mitrović, David Thomson
Lecture Notes in Computer Science  
In this paper, we look at initial results of data mining students' help-seeking behaviour in two ITSs: SQL-Tutor and EER-Tutor. We categorised help given by these tutors into high-level (HLH) and lowlevel help (LLH), depending on the amount of help given. Each student was grouped into one of ten groups based on the frequency with which they used HLH. Learning curves were then plotted for each group. We asked the question, "Does a student's help-seeking behaviour (especially the frequency with
more » ... ich they use HLH) affect learning?" We noticed similarities between results for both tutors. Students who were very frequent users of HLH showed the lowest learning, both in learning rates and depth of knowledge. Students who were low to medium users of HLH showed the highest learning rates. Least frequent users of HLH had lower learning rates but showed higher depth of knowledge and a lower initial error rate, suggesting higher initial expertise. These initial results could suggest favouring pedagogical strategies that provide low to medium HLH to certain students.
doi:10.1007/978-3-540-70987-9_42 fatcat:3iy3dqekivfu3khl3wvwpa3yfy